This package provides a tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.

Implemented are three Wald-type statistic and respective permuted versions for null hypotheses formulated in terms of cumulative hazard rate functions, medians and the concordance measure, respectively, in the general framework of survival factorial designs with possibly heterogeneous survival and/or censoring distributions, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Ditzhaus, Dobler and Pauly (2020) <doi:10.1177/0962280220980784> Ditzhaus, Janssen, Pauly (2020) <arXiv: 2004.10818v2> Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.

This package provides stat_isotonic() to add weighted univariate isotonic regression curves.

Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.

This package provides a ggplot2 based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using ggplot2 techniques.

Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. The training graph is used to estimate eigenvectors, and the test graph is used to evaluate the correlation between the training eigenvectors and the eigenvectors of the test graph. The correlations follow a simple central limit theorem that can be used to estimate graph dimension via hypothesis testing, see Chen et al. (2021) <doi:10.48550/arXiv.2108.03336> for details.

Preview what a ggplot2 plot would look like if you save it to a file. Attach picture dimensions as a canvas() element and get an instant preview. These dimensions will then be used when you save the plot.

Build a map of path-based geometry, this is a simple description of the number of parts in an object and their basic structure. Translation and restructuring operations for planar shapes and other hierarchical types require a data model with a record of the underlying relationships between elements. The gibble() function creates a geometry map, a simple record of the underlying structure in path-based hierarchical types. There are methods for the planar shape types in the sf and sp packages and for types in the trip and silicate packages.

This package provides a novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).

Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.

Bindings to the GeographicLib C++ library <https://geographiclib.sourceforge.io/> for precise geodetic calculations including geodesic computations (distance, bearing, paths, intersections), map projections (UTM/UPS, Transverse Mercator, Lambert Conformal Conic, and more), grid reference systems (MGRS, Geohash, GARS, Georef), coordinate conversions (geocentric, local Cartesian), and polygon area on the WGS84 ellipsoid. All functions are fully vectorized.

This package implements the most common Gaussian process (GP) models using Laplace and expectation propagation (EP) approximations, maximum marginal likelihood (or posterior) inference for the hyperparameters, and sparse approximations for larger datasets.

Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.

This package provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.

The method aims to identify important factors in screening experiments by aggregation over random models as studied in Singh and Stufken (2022) <doi:10.48550/arXiv.2205.13497>. This package provides functions to run the Gauss-Dantzig selector on screening experiments when interactions may be affecting the response. Currently, all functions require each factor to be at two levels coded as +1 and -1.

Create geographically referenced traffic data from the Google Maps JavaScript API <https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.

To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.

Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.

Introduces a Copilot'-like completion experience, but it knows how to talk to the objects in your R environment. ellmer chats are integrated directly into your RStudio and Positron sessions, automatically incorporating relevant context from surrounding lines of code and your global environment (like data frame columns and types). Open the package dialog box with a keyboard shortcut, type your request, and the assistant will stream its response directly into your documents.

Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.

This package provides tools for studying genotype-phenotype maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 <doi:10.3389/fgene.2013.00216>.

This package provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.

When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report.

Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects.